Fu Wei, Simonoff Jeffrey S
Leonard N. Stern School of Business, New York University, New York, USA.
Stat Med. 2017 Dec 30;36(30):4831-4842. doi: 10.1002/sim.7450. Epub 2017 Aug 18.
Interval-censored data, in which the event time is only known to lie in some time interval, arise commonly in practice, for example, in a medical study in which patients visit clinics or hospitals at prescheduled times and the events of interest occur between visits. Such data are appropriately analyzed using methods that account for this uncertainty in event time measurement. In this paper, we propose a survival tree method for interval-censored data based on the conditional inference framework. Using Monte Carlo simulations, we find that the tree is effective in uncovering underlying tree structure, performs similarly to an interval-censored Cox proportional hazards model fit when the true relationship is linear, and performs at least as well as (and in the presence of right-censoring outperforms) the Cox model when the true relationship is not linear. Further, the interval-censored tree outperforms survival trees based on imputing the event time as an endpoint or the midpoint of the censoring interval. We illustrate the application of the method on tooth emergence data.
区间删失数据是指事件发生时间仅已知位于某个时间区间内，这种数据在实际中很常见，例如在一项医学研究中，患者按预定时间前往诊所或医院就诊，而感兴趣的事件发生在就诊之间。此类数据适合使用考虑事件时间测量中这种不确定性的方法进行分析。在本文中，我们基于条件推断框架提出了一种用于区间删失数据的生存树方法。通过蒙特卡罗模拟，我们发现该树在揭示潜在树结构方面是有效的，当真实关系为线性时，其表现与区间删失的Cox比例风险模型拟合相似，而当真实关系非线性时，其表现至少与Cox模型一样好（并且在存在右删失的情况下优于Cox模型）。此外，区间删失树优于基于将事件时间估计为删失区间端点或中点的生存树。我们说明了该方法在牙齿萌出数据上的应用。
